summary.ddml_ate: Inference Methods for Treatment Effect Estimators.

In ddml: Double/Debiased Machine Learning

Inference Methods for Treatment Effect Estimators.

Description

Inference methods for treatment effect estimators. By default, standard errors are heteroskedasiticty-robust. If the ddml estimator was computed using a cluster_variable, the standard errors are also cluster-robust by default.

Usage

## S3 method for class 'ddml_ate'
summary(object, ...)

## S3 method for class 'ddml_att'
summary(object, ...)

## S3 method for class 'ddml_late'
summary(object, ...)

Arguments

object	An object of class ddml_ate, ddml_att, and ddml_late, as fitted by ddml_ate(), ddml_att(), and ddml_late(), respectively.
...	Currently unused.

Value

A matrix with inference results.

Examples

# Construct variables from the included Angrist & Evans (1998) data
y = AE98[, "worked"]
D = AE98[, "morekids"]
X = AE98[, c("age","agefst","black","hisp","othrace","educ")]

# Estimate the average treatment effect using a single base learner, ridge.
ate_fit <- ddml_ate(y, D, X,
                    learners = list(what = mdl_glmnet,
                                    args = list(alpha = 0)),
                    sample_folds = 2,
                    silent = TRUE)
summary(ate_fit)

ddml documentation built on Oct. 3, 2024, 1:13 a.m.